MachineShop: Machine Learning Models and Tools for R

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MachineShop is a meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Support is provided for predictive modeling of numerical, categorical, and censored time-to-event outcomes and for resample (bootstrap, cross-validation, and split training-test sets) estimation of model performance. This vignette introduces the package interface with a survival data analysis example, followed by supported methods of variable specification; applications to other response variable types; available performance metrics, resampling techniques, and graphical and tabular summaries; and modeling strategies.


Getting Started


# Current release from CRAN

# Development version from GitHub
# install.packages("devtools")

# Development version with vignettes
devtools::install_github("brian-j-smith/MachineShop", build_vignettes = TRUE)


Once installed, the following R commands will load the package and display its help system documentation. Online documentation and examples are available at the MachineShop website.


# Package help summary

# Vignette
RShowDoc("Introduction", package = "MachineShop")